Clear cell Renal Cell Carcinoma (ccRCC) is characterized by VHL inactivation1,2. Because no other gene is mutated as frequently, and VHL mutations are truncal3, VHL inactivation is regarded as the governing event4. VHL loss activates HIF-2, and constitutive HIF-2 restores tumorigenesis in VHL-reconstituted ccRCC cells5. HIF-2 is implicated in angiogenesis and multiple other processes6–9, but angiogenesis is the main target of drugs like sunitinib10. HIF-2, a transcription factor, has been regarded as undruggable11. A structure-based design approach identified a selective HIF-2 antagonist (PT2399) that we evaluate using a tumorgraft (TG)/PDX platform12,13. PT2399 dissociated HIF-2 (an obligatory heterodimer [HIF-2α/HIF-1β])14 in human ccRCC suppressing tumorigenesis in 56% (10/18) lines. PT2399 had greater activity than sunitinib, was active in sunitinib-progressing tumors, and was better tolerated. Unexpectedly, some VHL-mutant ccRCCs were resistant. Resistance occurred despite HIF-2 dissociation in tumors and evidence of Hif-2 inhibition in the mouse as determined by suppression of circulating erythropoietin, a HIF-2 target15 and possible pharmacodynamic marker. We identified a HIF-2-dependent gene signature in sensitive tumors. Illustrating drug specificity, gene expression was largely unaffected by PT2399 in resistant tumors. Sensitive tumors exhibited a distinguishing gene expression signature, and generally higher HIF-2α levels. Prolonged PT2399 treatment led to resistance. We identified a binding site and second site suppressor mutation in HIF-2α and HIF-1β respectively. Both mutations preserved HIF-2 dimers despite treatment with PT2399. Finally, an extensively pretreated patient with a sensitive TG had disease control for >11 months with the close analogue PT2385. We validate HIF-2 as a target in ccRCC, show that some ccRCC are, unexpectedly, HIF-2 independent, and set the stage for biomarker-driven clinical trials.

Anti-tumor immunity is driven by self vs. non-self discrimination. Many immunotherapeutic approaches to cancer have taken advantage of tumor neoantigens derived from somatic mutations. Here, we demonstrate that gene fusions are a source of immunogenic neoantigens that can mediate responses to immunotherapy. We identified an exceptional responder with metastatic head and neck cancer who experienced a complete response to immune checkpoint inhibitor therapy, despite a low mutational load and minimal pre-treatment immune infiltration in the tumor. Using whole genome sequencing (WGS) and RNA sequencing (RNA-seq), we identified a novel gene fusion, and demonstrated that it produces a neoantigen that can specifically elicit a host cytotoxic T cell response. In a cohort of head and neck tumors with low mutation burden, minimal immune infiltration, and prevalent gene fusions, we also identified gene fusion-derived neoantigens that generate cytotoxic T cell responses. Finally, analyzing additional datasets of fusion-positive cancers, including checkpoint inhibitor-treated tumors, we found evidence of immune surveillance resulting in negative selective pressure against gene fusion-derived neoantigens. These findings highlight an important class of tumor-specific antigens, and have implications for targeting gene fusion events in cancers that would otherwise be less poised for response to immunotherapy, including cancers with low mutational load and minimal immune infiltration.

Human papillomavirus (HPV) is a necessary but insufficient cause of a subset of oral squamous cell carcinomas (OSCCs) that is increasing markedly in frequency. To identify contributory, secondary genetic alterations in these cancers, we used comprehensive genomics methods to compare 149 HPV-positive and 335 HPV-negative OSCC tumor/normal pairs. Different behavioral risk factors underlying the two OSCC types were reflected in distinctive genomic mutational signatures. In HPV-positive OSCCs, the signatures of APOBEC cytosine deaminase editing, associated with anti-viral immunity, were strongly linked to overall mutational burden. In contrast, in HPV-negative OSCCs, T>C substitutions in the sequence context 5 ′-ATN-3 ′ correlated with tobacco exposure. Universal expression of HPV E6 * 1 and E7 oncogenes was a sine qua non of HPV-positive OSCCs. Significant enrichment of somatic mutations was confirmed or newly identified in PIK3CA, KMT2D,

Objective In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning. Methods Twenty histologic features were assessed in 129 synovial tissue samples (n = 123 RA patients and n = 6 osteoarthritis [OA] patients). Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes, using histologic data as the input. Corresponding clinical data were compared across subtypes. Results Consensus clustering of gene expression data revealed 3 distinct synovial subtypes, including a high inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including transforming growth factor β, glycoproteins, and neuronal genes, and a mixed subtype. Machine learning applied to histologic features, with gene expression subtypes serving as labels, generated an algorithm for the scoring of histologic features. Patients with the high inflammatory synovial subtype exhibited higher levels of markers of systemic inflammation and autoantibodies. C‐reactive protein (CRP) levels were significantly correlated with the severity of pain in the high inflammatory subgroup but not in the others. Conclusion Gene expression analysis of RA and OA synovial tissue revealed 3 distinct synovial subtypes. These labels were used to generate a histologic scoring algorithm in which the histologic scores were found to be associated with parameters of systemic inflammation, including the erythrocyte sedimentation rate, CRP level, and autoantibody levels. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes.

Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1 , but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs 2-5 . It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be fully recovered from whole-genome sequence (WGS) data on 21,620 unrelated individuals of European ancestry. We assigned 47.1 million genetic variants to groups based upon their minor allele frequencies (MAF) and linkage disequilibrium (LD) with variants nearby, and estimated and partitioned variation accordingly. The estimated heritability was 0.79 (SE 0.09) for height and 0.40 (SE 0.09) for BMI, consistent with pedigree estimates. Low-MAF variants in low LD with neighbouring variants were enriched for heritability, to a greater extent for protein altering variants, consistent with negative selection thereon. Cumulatively variants in the MAF range of 0.0001 to 0.1 explained 0.54 (SE 0.05) and 0.51 (SE 0.11) of heritability for height and BMI, respectively. Our results imply that the still missing heritability of complex traits and disease is accounted for by rare variants, in particular those in regions of low LD.